The document presents an EEG-based brain-computer interface (BCI) system for recognizing emotions in patients with disorders of consciousness. The BCI system uses EEG data to distinguish positive and negative emotions evoked by viewing video clips in both healthy controls and DOC patients. Results show the BCI system classified emotions with over 80% accuracy in controls and provided a potential method for assessing residual consciousness and brain function in DOC patients by recognizing their emotions.
di Paolo M. Rossini
Clinica Neurologica, Università Cattolica, Policlinico . Gemelli, Roma
Convegno "Le neuroscienze incontrano le altre discipline"
Padova, Palazzo del Bo
5 maggio 2011
Il convegno è promosso dall’Università di Padova e dal Dipartimento di Psicologia generale della stessa università, con il sostegno della Fondazione Sigma Tau e della Fondazione Giannino Bassetti.
Effect and maintenance of "EEG-biofeedback rTMS" on mood and working memory ...Amin Asadollahpour Kargar
This is a proposal presented in the 1st IBRO/APRC Iranian Associate School of Cognitive Neuroscience “Functional Human Brain Mapping”, Tehran, Iran, May 22-28, 2015
aimed:
1. To evaluate the effect of EEG-biofeedback rTMS on Mood in major depressed patients compare to EEG biofeedback and rTMS
2. To evaluate the maintenance of EEG-biofeedback rTMS on working memory in major depressed patients compare to EEG biofeedback and rTMS
di Paolo M. Rossini
Clinica Neurologica, Università Cattolica, Policlinico . Gemelli, Roma
Convegno "Le neuroscienze incontrano le altre discipline"
Padova, Palazzo del Bo
5 maggio 2011
Il convegno è promosso dall’Università di Padova e dal Dipartimento di Psicologia generale della stessa università, con il sostegno della Fondazione Sigma Tau e della Fondazione Giannino Bassetti.
Effect and maintenance of "EEG-biofeedback rTMS" on mood and working memory ...Amin Asadollahpour Kargar
This is a proposal presented in the 1st IBRO/APRC Iranian Associate School of Cognitive Neuroscience “Functional Human Brain Mapping”, Tehran, Iran, May 22-28, 2015
aimed:
1. To evaluate the effect of EEG-biofeedback rTMS on Mood in major depressed patients compare to EEG biofeedback and rTMS
2. To evaluate the maintenance of EEG-biofeedback rTMS on working memory in major depressed patients compare to EEG biofeedback and rTMS
HUMAN EMOTION ESTIMATION FROM EEG AND FACE USING STATISTICAL FEATURES AND SVMcsandit
An approach is presented in this paper for automated estimation of human emotions from
combination of multimodal data: electroencephalogram and facial images. The used EEG
features are the Hjorth parameters calculated for theta, alpha, beta and gamma bands taken
from pre-defined channels. For face emotion estimation PCA feature are selected. Classification
is performed with support vector machines. Since the human emotions are modelled as
combinations from physiological elements such as arousal, valence, dominance, liking, etc.,
these quantities are the classifier’s outputs. The best achieved correct classification
performance for EEG is about 76%. Classifier combination is used to return the final score for
the particular subject.
POWER SPECTRAL ANALYSIS OF EEG AS A POTENTIAL MARKER IN THE DIAGNOSIS OF SPAS...ijbesjournal
The detection and diagnosis of various neurological disorders are performed using different medical
devices among which electroencephalogram (EEG) is one of the most cost effective technique. Though
significant progress had been made in the analysis of EEG for diagnosis of different neurological
disorders, yet detection of cerebral palsy (CP) is not quite clear. This study was performed to analyze the
EEG power spectrum density (PSD) of spastic CP and normal children to find if any significant EEG
patterns could be used for early detection of CP. Twenty children participated in this study out of which ten
were spastic CP and other ten were normal healthy children. EEG of all the participants was recorded
from C3 C4 and F3 F4 regions following montage 10-20 system. The artifact-free EEG signals of 15
minutes duration was extracted for spectral analysis using Fast Fourier Transformation (FFT) algorithm
in MATLAB and power density spectrum (PSD) was plotted. The PSD revealed high intensity power peak
at frequency of 50Hz and smaller at 100 Hz, which was consistent for all healthy subjects. In case of
spastic CP children, high intensity peak at 100Hz were prominent and smaller peak was observed at 50Hz.
The high intensity 100Hz peak observed in the PSD of spastic CP patients demonstrated that this tool can
be used for early detection of spastic CP.
In this presentation I introduce TMS usage in neurocognitive research for the MSc course at Bangor School of Psychology. Note that some of the material comes from other useful presentations found online.
Amyotrophic Lateral Sclerosis (ALS) is the most common progressive neurodegenerative disorder reflecting
the degeneration of upper and lower motor neurons. Motor neurons controls the communication between nervous
system and muscles of the body. ALS results in the loss of voluntary control over muscular activities along with the
inability to breathe and the maximum life expectancy of affected individual will be 3-5 years from the onset of
symptoms. But the lifetime of affected people can be extended by early detection of disease. The usual methods for
diagnosis are Electromyography (EMG), Nerve Conduction Study (NCS), Magnetic Resonance Imaging (MRI) and
Magneto-encephalography (MEG). But some of these methods may erroneously result in neuropathy or myopathy
instead of ALS and some do not provide any biomarker. EEG is comparatively least expensive method and it
provides biomarker for ALS detection. ALS is always associated with fronto-temporal dementia (FTD). The spectral
analysis of EEG will reveal the structural and functional connectivity alterations of the underlying neural network
that occurs due to FTD and it can generate potential biomarkers for the early detection of ALS. A novel algorithm
has been developed by exploiting the Dual Tree Complex Wavelet Transform (DTCWT) technique and it can
overcome the short comes of existing methods for the analysis and feature extraction of EEG. Deterministic
biomarkers were obtained from spectral analysis of EEG and the proposed algorithm provided 100% accuracy for all
the test datasets.
Analysis of emotion disorders based on EEG signals ofHuman BrainIJCSEA Journal
In this research, the emotions and the patterns of EEG signals of human brain are studied. The aim of this research is to study the analysis of the changes in the brain signals in the domain of different emotions. The observations can be analysed for its utility in the diagnosis of psychosomatic disorders like anxiety and depression in economical way with higher precision.
A NALYSIS OF P AIN H EMODYNAMIC R ESPONSE U SING N EAR -I NFRARED S PECTROSCOPYijma
Despite recent advances in brain research, understa
nding the various signals for pain and pain intensi
ties
in the brain cortex is still a complex task due to
temporal and spatial variations of brain haemodynam
ics.
In this paper we have investigated pain based on ce
rebral hemodynamics via near-infrared spectroscopy
(NIRS). This study presents a pain stimulation expe
riment that uses three acupuncture manipulation
techniques to safely induce pain in healthy subject
s. Acupuncture pain response was presented and
Haemodynamic pain signal analysis showed the presence of dominant channels and their relationship
among surrounding channels, which contribute the fu
rther pain research area.
Abnormal changes in cortical activity in women with migraine bet.docxdaniahendric
Abnormal changes in cortical activity in women with migraine between episodes
subject
Migraine group: 21 female migraine patients (mean age: 34.6 + 6.7 years), all with right hand, ranged from 1 year to 22 years.
Healthy control group: 10 females (mean age: 30.2 + 4.3 years), all of whom were right-handed, and their education degree matched that of the migraine group
task
Flip board
Is generated by
Direct(version 9; Microsoft Inc., Redmond, WA, USA)Brain X, The white board is made up of black and white children and yellow dots at the center , The brightness of the white checkerboard is
80cd/m², The flip frequency is
1HZ,( There is a 600ms interval between the two stimuli and the duration of the stimulus is 400ms ), The image was projected by a projector about 32cm from the subject's nasal roots , Participants' perspectives and board into 30 °.
The standard set
Criteria for inclusion of migraine patients:
(1) According to the international classification of headache diseases edition 2 (ICHD).II) criteria for the diagnosis of unthreatened migraine and confirmed by neurologists
(2) Exclusion of other neurological diseases;
(3) The patient had no history of visual system and eye diseases
(4) Patients with the disease were not treated with migraine preventive drugs within 1 week before the test.
(5) No migraine occurred 72 hours before, on the day of and 24 hours after MEG examination
Inclusion criteria of healthy control group:
(1) No history of visual system-related diseases;
(2) No history of neuropsychiatric disorders
;
(3) His first-degree relatives had no history of migraine
。
Exclusion criteria for both groups:
Patients with metal implants (severely interfering with the brain magnetogram signal);
A history of other major neurological or psychiatric disorders ;
A history of extensive developmental disorders
;
A history of clinically significant systemic diseases
Cannot stay still during MEG recording and MR/scan
Don't move;
;
Subjects with claustrophobia or during pregnancy
DATA analysis ----No 1Morphology
(waveform):VEFII Is the most stable of visual evoked potential composition, therefore, the main observation indexes for this experiment, the incubation period the VEFII approximately 2.5 -100 hz frequency (latency) is relatively more clearly and wave (Amplitude) interference factors, and can reflect the strength of the temporary only, reason is not included in the result analysis.
Result:
Result --waveform
The composition of VEF II was longer in the migraine group than in the healthy control group (HC)
122±3.02ms VS 118±3.78ms(p<0.05)
trigger
Sensor level analysis
(Time–frequency analysis)
1.By Morlet continuous wavelet(CWT)polarity contour maps can be showed to Study the spatial characteristics of visual evoked potential
2. Measuring the energy characteristics of the visually induced magnetic field VEF: select the most representative channel and calculate the absolute wave energy of the VEF measured by its root- ...
Detection of Type-B Artifacts in VEPs using Median Deviation AlgorithmIOSRJECE
The primary goal of this research work is to introduce temporal artifact detection strategy to detect non responsive channels and trials in visual evoked potentials(VEPs) by tracing out the signals with very low energy and to remove artifacts in multichannel visual evoked potentials. The non responsive channels and trials are identified by calculating the energy of the average evoked potential of each channel, and the energy of the average evoked potential of each trial. Then channel wise and trial wise median test is conducted to detect and remove non-responsive channels and trials. An artifact is defined as any signal that may lead to inaccurate classifier parameter estimation. Temporal domain artifact detection tests include: a clipping (CL) test detect amplitude clipped EPs in each channel, a standard deviation (STD) test that can detect signals with little or abnormal variations in each channel, a kurtosis (KU) test to detect unusual signals that are not identified by STD and CL tests and median deviation test to detect signals containing large number of samples with very small deviation from their normal values. An attempt has been made to apply these techniques to 14-channel visual evoked potentials (VEPs) obtained from different subjects.
HUMAN EMOTION ESTIMATION FROM EEG AND FACE USING STATISTICAL FEATURES AND SVMcsandit
An approach is presented in this paper for automated estimation of human emotions from
combination of multimodal data: electroencephalogram and facial images. The used EEG
features are the Hjorth parameters calculated for theta, alpha, beta and gamma bands taken
from pre-defined channels. For face emotion estimation PCA feature are selected. Classification
is performed with support vector machines. Since the human emotions are modelled as
combinations from physiological elements such as arousal, valence, dominance, liking, etc.,
these quantities are the classifier’s outputs. The best achieved correct classification
performance for EEG is about 76%. Classifier combination is used to return the final score for
the particular subject.
POWER SPECTRAL ANALYSIS OF EEG AS A POTENTIAL MARKER IN THE DIAGNOSIS OF SPAS...ijbesjournal
The detection and diagnosis of various neurological disorders are performed using different medical
devices among which electroencephalogram (EEG) is one of the most cost effective technique. Though
significant progress had been made in the analysis of EEG for diagnosis of different neurological
disorders, yet detection of cerebral palsy (CP) is not quite clear. This study was performed to analyze the
EEG power spectrum density (PSD) of spastic CP and normal children to find if any significant EEG
patterns could be used for early detection of CP. Twenty children participated in this study out of which ten
were spastic CP and other ten were normal healthy children. EEG of all the participants was recorded
from C3 C4 and F3 F4 regions following montage 10-20 system. The artifact-free EEG signals of 15
minutes duration was extracted for spectral analysis using Fast Fourier Transformation (FFT) algorithm
in MATLAB and power density spectrum (PSD) was plotted. The PSD revealed high intensity power peak
at frequency of 50Hz and smaller at 100 Hz, which was consistent for all healthy subjects. In case of
spastic CP children, high intensity peak at 100Hz were prominent and smaller peak was observed at 50Hz.
The high intensity 100Hz peak observed in the PSD of spastic CP patients demonstrated that this tool can
be used for early detection of spastic CP.
In this presentation I introduce TMS usage in neurocognitive research for the MSc course at Bangor School of Psychology. Note that some of the material comes from other useful presentations found online.
Amyotrophic Lateral Sclerosis (ALS) is the most common progressive neurodegenerative disorder reflecting
the degeneration of upper and lower motor neurons. Motor neurons controls the communication between nervous
system and muscles of the body. ALS results in the loss of voluntary control over muscular activities along with the
inability to breathe and the maximum life expectancy of affected individual will be 3-5 years from the onset of
symptoms. But the lifetime of affected people can be extended by early detection of disease. The usual methods for
diagnosis are Electromyography (EMG), Nerve Conduction Study (NCS), Magnetic Resonance Imaging (MRI) and
Magneto-encephalography (MEG). But some of these methods may erroneously result in neuropathy or myopathy
instead of ALS and some do not provide any biomarker. EEG is comparatively least expensive method and it
provides biomarker for ALS detection. ALS is always associated with fronto-temporal dementia (FTD). The spectral
analysis of EEG will reveal the structural and functional connectivity alterations of the underlying neural network
that occurs due to FTD and it can generate potential biomarkers for the early detection of ALS. A novel algorithm
has been developed by exploiting the Dual Tree Complex Wavelet Transform (DTCWT) technique and it can
overcome the short comes of existing methods for the analysis and feature extraction of EEG. Deterministic
biomarkers were obtained from spectral analysis of EEG and the proposed algorithm provided 100% accuracy for all
the test datasets.
Analysis of emotion disorders based on EEG signals ofHuman BrainIJCSEA Journal
In this research, the emotions and the patterns of EEG signals of human brain are studied. The aim of this research is to study the analysis of the changes in the brain signals in the domain of different emotions. The observations can be analysed for its utility in the diagnosis of psychosomatic disorders like anxiety and depression in economical way with higher precision.
A NALYSIS OF P AIN H EMODYNAMIC R ESPONSE U SING N EAR -I NFRARED S PECTROSCOPYijma
Despite recent advances in brain research, understa
nding the various signals for pain and pain intensi
ties
in the brain cortex is still a complex task due to
temporal and spatial variations of brain haemodynam
ics.
In this paper we have investigated pain based on ce
rebral hemodynamics via near-infrared spectroscopy
(NIRS). This study presents a pain stimulation expe
riment that uses three acupuncture manipulation
techniques to safely induce pain in healthy subject
s. Acupuncture pain response was presented and
Haemodynamic pain signal analysis showed the presence of dominant channels and their relationship
among surrounding channels, which contribute the fu
rther pain research area.
Abnormal changes in cortical activity in women with migraine bet.docxdaniahendric
Abnormal changes in cortical activity in women with migraine between episodes
subject
Migraine group: 21 female migraine patients (mean age: 34.6 + 6.7 years), all with right hand, ranged from 1 year to 22 years.
Healthy control group: 10 females (mean age: 30.2 + 4.3 years), all of whom were right-handed, and their education degree matched that of the migraine group
task
Flip board
Is generated by
Direct(version 9; Microsoft Inc., Redmond, WA, USA)Brain X, The white board is made up of black and white children and yellow dots at the center , The brightness of the white checkerboard is
80cd/m², The flip frequency is
1HZ,( There is a 600ms interval between the two stimuli and the duration of the stimulus is 400ms ), The image was projected by a projector about 32cm from the subject's nasal roots , Participants' perspectives and board into 30 °.
The standard set
Criteria for inclusion of migraine patients:
(1) According to the international classification of headache diseases edition 2 (ICHD).II) criteria for the diagnosis of unthreatened migraine and confirmed by neurologists
(2) Exclusion of other neurological diseases;
(3) The patient had no history of visual system and eye diseases
(4) Patients with the disease were not treated with migraine preventive drugs within 1 week before the test.
(5) No migraine occurred 72 hours before, on the day of and 24 hours after MEG examination
Inclusion criteria of healthy control group:
(1) No history of visual system-related diseases;
(2) No history of neuropsychiatric disorders
;
(3) His first-degree relatives had no history of migraine
。
Exclusion criteria for both groups:
Patients with metal implants (severely interfering with the brain magnetogram signal);
A history of other major neurological or psychiatric disorders ;
A history of extensive developmental disorders
;
A history of clinically significant systemic diseases
Cannot stay still during MEG recording and MR/scan
Don't move;
;
Subjects with claustrophobia or during pregnancy
DATA analysis ----No 1Morphology
(waveform):VEFII Is the most stable of visual evoked potential composition, therefore, the main observation indexes for this experiment, the incubation period the VEFII approximately 2.5 -100 hz frequency (latency) is relatively more clearly and wave (Amplitude) interference factors, and can reflect the strength of the temporary only, reason is not included in the result analysis.
Result:
Result --waveform
The composition of VEF II was longer in the migraine group than in the healthy control group (HC)
122±3.02ms VS 118±3.78ms(p<0.05)
trigger
Sensor level analysis
(Time–frequency analysis)
1.By Morlet continuous wavelet(CWT)polarity contour maps can be showed to Study the spatial characteristics of visual evoked potential
2. Measuring the energy characteristics of the visually induced magnetic field VEF: select the most representative channel and calculate the absolute wave energy of the VEF measured by its root- ...
Detection of Type-B Artifacts in VEPs using Median Deviation AlgorithmIOSRJECE
The primary goal of this research work is to introduce temporal artifact detection strategy to detect non responsive channels and trials in visual evoked potentials(VEPs) by tracing out the signals with very low energy and to remove artifacts in multichannel visual evoked potentials. The non responsive channels and trials are identified by calculating the energy of the average evoked potential of each channel, and the energy of the average evoked potential of each trial. Then channel wise and trial wise median test is conducted to detect and remove non-responsive channels and trials. An artifact is defined as any signal that may lead to inaccurate classifier parameter estimation. Temporal domain artifact detection tests include: a clipping (CL) test detect amplitude clipped EPs in each channel, a standard deviation (STD) test that can detect signals with little or abnormal variations in each channel, a kurtosis (KU) test to detect unusual signals that are not identified by STD and CL tests and median deviation test to detect signals containing large number of samples with very small deviation from their normal values. An attempt has been made to apply these techniques to 14-channel visual evoked potentials (VEPs) obtained from different subjects.
Push towards digital Library .Data collection through survey. Small scale experiment .Proceeded on library area .Building a world wide digital library.
Global routing topology.
loop-free paths . Dynamic distributed algorithm was rooted on this algorithm . Take Less Recovery Time(h) than previous algorithm (h^2) when Network Links fails .
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
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Acetabularia Information For Class 9 .docxvaibhavrinwa19
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Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
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Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
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This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
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Nafiz prasented an eeg-based brain computer interface for
1. AN EEG-BASED BRAIN COMPUTER
INTERFACE FOR
EMOTION RECOGNITION AND ITS
APPLICATION IN
PATIENTS WITH DISORDER OF
CONSCIOUSNESS
HAIYUN HUANG, QIUYOU XIE, JIAHUI PAN, YANBIN HE, ZHENFU WEN,
RONGHAO YU AND YUANQING LI,
2019
PRESENTED BY
NAFIZ ISHTIAQUE AHMED
23-JANUARY-2020
UOU- UNIVERSITY OF ULSAN
Computational
Neural
Engineering Lab.
2. DISORDER OF CONSCIOUSNESS (DOC)
Brain Injuries :
Coma,
Vegetative state (VS),
Minimal conscious state
(MCS),
Emergence from MCS (EMCS)
3. INTRODUCTION
Emotion has strong connection with Consciousness.
If DOC patients emotion is recognized then it can help to
assess their residual consciousness as well as their impaired
brain functions.
As DOC patients suffers from severe motor impairments and
unable to provide proper emotion expressions; So far doctors
cannot detect their emotional states.
4. EMOTION RECOGNITION
Evoked Mechanisms Accuracy
Viewing Picture EEG 80.77%
listening to music EEG 82.29% ± 3.06%
Watching videos EEG, pupillary responses,
and gaze distances
76.4%
Watching videos EEG 79.28%
listening to music
(Real-time)
EEG 53.96%
Emotion Recognition of Healthy people.
5. INTRODUCTION
EEG is significant and widely used for identifying
human emotion state.
This paper shows EEG Based BCI system to
recognize
the emotions of DOC patients at real-time.
Emotion evoked by 2 class video clips
Positive
Negative
6. SUBJECTS
Control group
(validate the BCI system)
10 student (8-male, 2-female)
Mean age 26
Normal vision and hearing
DOC Patient
(applied the BCI system)
8 patient (6-male, 2-female)
Mean age 35
Stable condition with normal vision
and hearing
No psychiatric medications (2-days)
Clinical diagnosis
2 patient-VS,
5 patient-MSC,
1 patient-EMCS
7. STIMULUS
Initially, 140 Chinese movie clips (30s) that contained positive
or negative scenes was collected.
10 volunteers evaluate their emotions with a level (i.e., not at
all, slightly, or extremely) and a keywords (i.e., positive or
negative) while watching the clips.
Finally, 40 Chinese video clips(20 positive ,20 negative ) that all
volunteers scored as extremely positive or negative were
selected.
Only 2 emotional state is chosen because complex and many
emotional states may increase the burden on the patients.
9. DATA ANALYSIS
Baseline corrected by subtracting the mean value of the 1s
signal before the stimulus start.
Notch filter was applied to remove the 50 Hz power-line noise.
Tenth order minimum-phase FIR bandpass filter between 0.1 to
70 Hz.
Online - Spectral power – STFT - a non-overlapped Hanning
window of 1 second- band power values are calculated by
averaging the power values in each frequency bands -
logarithmic scale – SVM model – Prediction.
Offline - preprocessing, feature extraction -classification
procedures are the same as online method - 10 times 5-fold
cross-validation.
12. Topographical maps of the classification weight of each electrode :
average of the weights of all five subbands
1. The left frontal areas correlated to
positive emotion.
2. The right hemisphere mainly processed
negative emotion .
3. The reported frontal midline areas were
associated with the process of positive
emotion.
13. Topographies of different frequency bands
1. Depicts the average power changes for negative
and positive emotions in the five bands (delta,
theta, alpha, beta, and gamma).
2. In the delta band, the right anterior areas were
activated more for positive emotion than for
negative emotion.
3. In the theta band, the prefrontal regions and
occipital lobe show higher power during positive
emotional state than during negative emotional
state.
4. In the alpha band, the power decreased in the right
frontal areas during negative emotion, the power of
the frontal areas increased during positive emotion.
5. In the beta and gamma bands, the power in the
lateral temporal areas for positive emotion was
significantly higher than that for negative emotion.
16. CONCLUSION
1. An EEG-based BCI system to distinguish video-induced positive
and negative emotions.
2. Positive & Negative emotions were well evoked and recognized
by this BCI system.
3. This system provides an potential approach to detect the
emotions in patients with DOC.
4. The emotion BCI system may be a potential tool for evaluating
the consciousness levels of patients with DOC.
Editor's Notes
Coma
A coma is when a person shows no signs of being awake and no signs of being aware.
A person in a coma lies with their eyes closed and doesn't respond to their environment, voices or pain.
Vegetative state
awake but is showing no signs of awareness.
open their eyes
wake up and fall asleep at regular intervals
have basic reflexes (such as blinking when they're startled by a loud noise or withdrawing their hand when it's squeezed hard).
Minimally conscious state
A person who shows inconsistent awareness.
They may have periods where they can communicate or respond to commands, such as moving a finger when asked.
the research of emotion recognition in patients with DOC may help us assess their residual consciousness and the impaired brain functions.
However, none of the existing studies has developed an EEG-based emotion recognition system for patients with DOC.
Then, the video clip that represents a positive/negative emotion, respectively, is played and the EEG data are collected and processed simultaneously. Then, the online recognition result is displayed on the screen as feedback. In this study, a smiling/crying cartoon face is presented as feedback, which represents the detection of a positive/negative emotion, respectively.
5 session total 10 trails and 10 test
several differences between the experimental procedures for DOC patients compared with healthy subject like braek time depending upon patients condition
the research of emotion recognition in patients with DOC may help us assess their residual consciousness and the impaired brain functions.
the importance of the theta band in emotion recognition
P4 is EMCS patient thus his emotion reorganization rate is higher
Patient P4 achieved the highest online accuracy among all patients, The electrodes that correlated with the top-20 features were mainly located in the temporal lobe, central area and occipital lobe.
For positive emotion, the frontal midline had a significant higher theta response.
Meanwhile, as shown in Fig. 3(b), the parietal and frontal areas were activated more in the alpha band in response to positive emotion.
In the beta and gamma frequency bands, the occipital lobe presented greater activation for positive emotional state than negative emotional state